Papers

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Viewing 1-10 of 1025 papers
  • ADaPT: As-Needed Decomposition and Planning with Language Models

    Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, Tushar KhotNAACL Findings2024 Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. Recent works employ LLMs-as-agents in broadly two ways: iteratively determining the next action (iterative…
  • Evaluating In-Context Learning of Libraries for Code Generation

    Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep DasigiNAACL2024 Contemporary Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. A particularly promising area is their ability to interpret code modules from unfamiliar libraries for solving user-instructed tasks. Recent work…
  • Impossible Distillation: from Low-Quality Model to High-Quality Dataset&Model for Summarization and Paraphrasing

    Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian R. Fisher, Taylor Sorensen, Yejin ChoiNAACL2024 We present Impossible Distillation, a novel framework for paraphrasing and sentence summarization, that distills a high-quality dataset and model from a low-quality teacher that itself cannot perform these tasks. Unlike prior works that rely on an extreme…
  • JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models

    Jillian R. Fisher, Ximing Lu, Jaehun Jung, Liwei Jiang, Zaid Harchaoui, Yejin ChoiNAACL2024 The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of online authorship when needed, e.g., blind reviews for scientific papers…
  • Leveraging Code to Improve In-context Learning for Semantic Parsing

    Ben Bogin, Shivanshu Gupta, Peter Clark, Ashish SabharwalNAACL2024 In-context learning (ICL) is an appealing approach for semantic parsing due to its few-shot nature and improved generalization. However, learning to parse to rare domain-specific languages (DSLs) from just a few demonstrations is challenging, limiting the…
  • MacGyver: Are Large Language Models Creative Problem Solvers?

    Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze BrahmanNAACL2024 We explore the creative problem-solving capabilities of modern LLMs in a novel constrained setting. To this end, we create MACGYVER, an automatically generated dataset consisting of over 1,600 real-world problems deliberately designed to trigger innovative…
  • NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge

    Phillip Howard, Junlin Wang, Vasudev Lal, Gadi Singer, Yejin Choi, Swabha SwayamdiptaNAACL2024 Comparative knowledge (e.g., steel is stronger and heavier than styrofoam) is an essential component of our world knowledge, yet understudied in prior literature. In this paper, we harvest the dramatic improvements in knowledge capabilities of language models…
  • On-the-fly Definition Augmentation of LLMs for Biomedical NER

    Monica Munnangi, Sergey Feldman, Byron C Wallace, Silvio Amir, Tom Hope, Aakanksha NaikNAACL 20242024 Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In this work we set out to improve LLM performance on biomedical NER in limited data…
  • Personalized Jargon Identification for Enhanced Interdisciplinary Communication

    Yue Guo, Joseph Chee Chang, Maria Antoniak, Erin Bransom, Trevor Cohen, Lucy Lu Wang, Tal AugustNAACL2024 Scientific jargon can impede researchers when they read materials from other domains. Current methods of jargon identification mainly use corpus-level familiarity indicators (e.g., Simple Wikipedia represents plain language). However, researchers' familiarity…
  • Promptly Predicting Structures: The Return of Inference

    Maitrey Mehta, Valentina Pyatkin, Vivek SrikumarNAACL2024 Prompt-based methods have been used extensively across NLP to build zero- and few-shot label predictors. Many NLP tasks are naturally structured: that is, their outputs consist of multiple labels which constrain each other. Annotating data for such tasks can…